基于数值天气预报的光伏组件温度预测研究  

RESEARCH ON PHOTOVOLTAIC MODULE TEMPERATURE PREDICTION BASED ON NWP DATA

作　　者：崔杨 成驰 陈正洪 Cui Yang;Cheng Chi;Chen Zhenghong(Hubei Meteorological Service Center,Wuhan 430205,China;Meteorological Energy Development Center of Hubei Province,Wuhan 430205,China)

机构地区：[1]湖北省气象服务中心,武汉430205 [2]湖北省气象能源技术开发中心,武汉430205

出　　处：《太阳能学报》2021年第5期202-208,共7页Acta Energiae Solaris Sinica

基　　金：湖北省气象局青年科技基金(2018Q09)。

摘　　要：为解决目前实验仿真中通常采用环境温度或固定数值替代光伏组件温度,导致建模结果偏差较大的问题,该文基于数值天气预报(NWP),采用自适应偏最小二乘回归法、BP神经网络法和集合预报法建立光伏组件温度预报模型,对17块不同倾角的光伏组件温度进行分析和预测。结果表明,集合预报法的预测效果最佳且具有普适性,在秋冬季,0°~25°倾角及西墙90°预测误差较低,均方根误差RMSE不超过3℃;春夏季各倾角误差变化相对平稳,春季集合预测法各倾角误差在2.1℃(0°)~3.0℃(45°)之间;夏季从倾角0°至90°,RMSE整体呈下降趋势,90°RMSE最低为1.9℃;对于不同的天气状况,晴天条件下预测误差相对最小,阴天和雨天对不同倾角预测误差的波动性较大;最后,针对武汉地区高温日和低温日的最佳倾角进行验证,高温日的板温预报RMSE为1.2℃,低温日为1.6℃。The current experimental simulations usually use environmental temperature or fixed value instead of photovoltaic module temperature which result in a deviation of modeling results.To solve this problem,the paper is based on numerical weather prediction(NWP),takes use of adaptive partial least squares regression method(APLSR),BP neural network method and ensemble prediction method to establish a prediction model of PV module temperature,to analysis and predict temperature of 17 PV modules with different angles.The result show that,The ensemble prediction method has the best prediction effect.In autumn and winter,the prediction error of 0°-25°inclination angle and the west wall 90°are lower than the others,with RMSE no more than 3℃.The variation of inclination error in spring and summer is relatively stable,as for ensemble prediction method,the RMSE is between 2.1℃(0°)and 3.0℃(45°)in spring;as for summer,from 0°to 90°,the RMSE decreases,and RMSE of 90°is the lowest(1.9℃);for different weather conditions,the prediction error is relatively minimal in sunny day,and the prediction error fluctuates in raining and cloudy days.Finally,the paper also verified the optimal title angle of high and low temperature days in Wuhan.The RMSE in high temperature day is1.2℃and in low temperature day is 1.6℃.

关 键 词：数值天气预报 神经网络法 光伏组件 线性回归 自适应偏最小二乘回归法 集合预报法 

分 类 号：TK519[动力工程及工程热物理—热能工程]